
"""simple docstring"""





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class        _UpperCAmelCase:



		@property
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							return self.get_dummy_input()



		@property
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							if self.block_type == "down":
												return (4, 32, 16, 16)
							elif self.block_type == "mid":
												return (4, 32, 32, 32)
							elif self.block_type == "up":
												return (4, 32, 64, 64)

							raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''')



		def 		UpperCAmelCase				(   self							,						__a=True							,						__a=False							,						__a=False							,						__a=False							,						)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    4
							_UpperCamelCase									=    32
							_UpperCamelCase									=    (32, 32)

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    torch.device(lowerCAmelCase__)
							_UpperCamelCase									=    (batch_size, num_channels) + sizes
							_UpperCamelCase									=    randn_tensor(lowerCAmelCase__							,						generator=lowerCAmelCase__							,						device=lowerCAmelCase__)
							_UpperCamelCase									=    {'''hidden_states''': hidden_states}

							if include_temb:
												_UpperCamelCase									=    1_28
												_UpperCamelCase									=    randn_tensor((batch_size, temb_channels)							,						generator=lowerCAmelCase__							,						device=lowerCAmelCase__)

							if include_res_hidden_states_tuple:
												_UpperCamelCase									=    torch.manual_seed(1)
												_UpperCamelCase									=    (randn_tensor(lowerCAmelCase__							,						generator=lowerCAmelCase__							,						device=lowerCAmelCase__),)

							if include_encoder_hidden_states:
												_UpperCamelCase									=    floats_tensor((batch_size, 32, 32)).to(lowerCAmelCase__)

							if include_skip_sample:
												_UpperCamelCase									=    randn_tensor(((batch_size, 3) + sizes)							,						generator=lowerCAmelCase__							,						device=lowerCAmelCase__)

							return dummy_input



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''in_channels''': 32,
							    '''out_channels''': 32,
							    '''temb_channels''': 1_28,
							}
							if self.block_type == "up":
												_UpperCamelCase									=    32

							if self.block_type == "mid":
												init_dict.pop('''out_channels''')

							_UpperCamelCase									=    self.dummy_input
							return init_dict, inputs_dict



		def 		UpperCAmelCase				(   self							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_init_args_and_inputs_for_common()
							_UpperCamelCase									=    self.block_class(**lowerCAmelCase__)
							unet_block.to(lowerCAmelCase__)
							unet_block.eval()

							with torch.no_grad():
												_UpperCamelCase									=    unet_block(**lowerCAmelCase__)

							if isinstance(lowerCAmelCase__							,						lowerCAmelCase__):
												_UpperCamelCase									=    output[0]

							self.assertEqual(output.shape							,						self.output_shape)

							_UpperCamelCase									=    output[0, -1, -3:, -3:]
							_UpperCamelCase									=    torch.tensor(lowerCAmelCase__).to(lowerCAmelCase__)
							assert torch_all_close(output_slice.flatten()							,						lowerCAmelCase__							,						atol=5e-3)




		@unittest.skipIf(torch_device == '''mps'''							,						'''Training is not supported in mps''')
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_init_args_and_inputs_for_common()
							_UpperCamelCase									=    self.block_class(**lowerCAmelCase__)
							model.to(lowerCAmelCase__)
							model.train()
							_UpperCamelCase									=    model(**lowerCAmelCase__)

							if isinstance(lowerCAmelCase__							,						lowerCAmelCase__):
												_UpperCamelCase									=    output[0]

							_UpperCamelCase									=    torch.device(lowerCAmelCase__)
							_UpperCamelCase									=    randn_tensor(output.shape							,						device=lowerCAmelCase__)
							_UpperCamelCase									=    torch.nn.functional.mse_loss(lowerCAmelCase__							,						lowerCAmelCase__)
							loss.backward()











'''simple docstring'''



from math import pow
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		):
							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							SCREAMING_SNAKE_CASE       :   Tuple         =      int(pow(A       ,		A					)					)
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
							return current_sum, solutions_count
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
														raise ValueError(
														    '''Invalid input\n'''
														    '''needed_sum must be between 1 and 1000, power between 2 and 10.'''					)

							return backtrack(A       ,		A       ,		1       ,		0       ,		0					)[1]  # Return the solutions_count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




